
#importing necessary libraries
import pandas as pd
import numpy as np


import matplotlib.pyplot as plt
import seaborn as sns
# to show the figures in the jupyter notebook itself
%matplotlib inline


train = pd.read_csv('train_bikes.csv', parse_dates=['datetime']) # loading the training data

train.head()


train.tail() # looking at the training data from end


train.plot.scatter(x = 'season', y = 'count') # plotting the counts based on the season

<AxesSubplot:xlabel='season', ylabel='count'>


train.plot.scatter(x = 'holiday', y = 'count') # plotting the counts based on the holidays

<AxesSubplot:xlabel='holiday', ylabel='count'>


train.plot.scatter(x = 'workingday', y = 'count') # plotting the counts based on working day

<AxesSubplot:xlabel='workingday', ylabel='count'>


train.plot.scatter(x = 'weather', y = 'count') # plotting the counts based on the weather

<AxesSubplot:xlabel='weather', ylabel='count'>


train.plot.scatter(x = 'temp', y = 'count') # plotting the counts based on the temparature

<AxesSubplot:xlabel='temp', ylabel='count'>


train.plot.scatter(x = 'atemp', y = 'count') # plotting the counts based on atemp

<AxesSubplot:xlabel='atemp', ylabel='count'>


train.plot.scatter(x = 'humidity', y = 'count')# plotting the counts based on humidity

<AxesSubplot:xlabel='humidity', ylabel='count'>


train.plot.scatter(x = 'windspeed', y = 'count') # plotting the counts based on windspeed

<AxesSubplot:xlabel='windspeed', ylabel='count'>


train.plot.scatter(x = 'casual', y = 'count')# plotting the counts based casual user

<AxesSubplot:xlabel='casual', ylabel='count'>


train.info() # observing the data types of the columns

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10886 entries, 0 to 10885
Data columns (total 12 columns):
 #   Column      Non-Null Count  Dtype         
---  ------      --------------  -----         
 0   datetime    10886 non-null  datetime64[ns]
 1   season      10886 non-null  int64         
 2   holiday     10886 non-null  int64         
 3   workingday  10886 non-null  int64         
 4   weather     10886 non-null  int64         
 5   temp        10886 non-null  float64       
 6   atemp       10886 non-null  float64       
 7   humidity    10886 non-null  int64         
 8   windspeed   10886 non-null  float64       
 9   casual      10886 non-null  int64         
 10  registered  10886 non-null  int64         
 11  count       10886 non-null  int64         
dtypes: datetime64[ns](1), float64(3), int64(8)
memory usage: 1020.7 KB


train.describe() # Generate descriptive statistics that summarize the central tendency,dispersion and shape of a dataset's distribution


test = pd.read_csv('test_bikes.csv') # loading the test data


test.head()  #looking at the 1st 5 rows of the test data


test.tail() # last 5 rows of the test data


test.info() # observing the data types of the columns for test data

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6493 entries, 0 to 6492
Data columns (total 9 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   datetime    6493 non-null   object 
 1   season      6493 non-null   int64  
 2   holiday     6493 non-null   int64  
 3   workingday  6493 non-null   int64  
 4   weather     6493 non-null   int64  
 5   temp        6493 non-null   float64
 6   atemp       6493 non-null   float64
 7   humidity    6493 non-null   int64  
 8   windspeed   6493 non-null   float64
dtypes: float64(3), int64(5), object(1)
memory usage: 456.7+ KB


test.describe() # Generate descriptive statistics that summarize the central tendency,dispersion and shape of a dataset's distribution for test data


# installing the pandas profiling library. It is used for a deeper understanding than the normal Dataframe.describe() method
# !pip install pandas-profiling


import pandas_profiling


train.profile_report()


print("count samples & features: ", train.shape) # printing the number of rows and columns
print("Are there missing values: ", train.isnull().values.any()) # printing if dataset has any NaN value

count samples & features:  (10886, 12)
Are there missing values:  False


# method for creating the count plot based on hour for a given year 
def plot_by_hour(data, year=None, agg='sum'):
    dd = data
    if year: dd = dd[ dd.datetime.dt.year == year ]
    dd.loc[:, ('hour')] = dd.datetime.dt.hour # extracting the hour data if the year in the data is equal to the year passed as argument
    
    by_hour = dd.groupby(['hour', 'workingday'])['count'].agg(agg).unstack() # groupby hour and working day
    return by_hour.plot(kind='bar', ylim=(0, 80000), figsize=(15,5), width=0.9, title="Year = {0}".format(year)) # returning the figure grouped by hour


plot_by_hour(train, year=2011) # plotting the count plot based on hour for 2011 
plot_by_hour(train, year=2012) # plotting the count plot based on hour for 2012

C:\Users\virat\Anaconda3\lib\site-packages\pandas\core\indexing.py:362: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  self.obj[key] = _infer_fill_value(value)
C:\Users\virat\Anaconda3\lib\site-packages\pandas\core\indexing.py:543: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  self.obj[item] = s

<matplotlib.axes._subplots.AxesSubplot at 0x19e36645e48>


# method for creating the count plot based on year 
def plot_by_year(agg_attr, title):
    # extracting the required fields
    dd = train.copy()
    dd['year'] = train.datetime.dt.year # extratcing the year
    dd['month'] = train.datetime.dt.month # extratcing the month
    dd['hour'] = train.datetime.dt.hour # extratcing the hour
    
    by_year = dd.groupby([agg_attr, 'year'])['count'].agg('sum').unstack() # groupby year
    return by_year.plot(kind='bar', figsize=(15,5), width=0.9, title=title) # returning the figure grouped by year


plot_by_year('month', "Rent bikes per month in 2011 and 2012") # plotting monthly bike rentals based on year
plot_by_year('hour', "Rent bikes per hour in 2011 and 2012") # plotting hourls bike rentals based  on year

<matplotlib.axes._subplots.AxesSubplot at 0x19e34b974e0>


# method to plot a graph for count per hour
def plot_hours(data, message = ''):
    dd = data.copy()
    dd['hour'] = data.datetime.dt.hour # extratcing the hour
    
    hours = {}
    for hour in range(24):
        hours[hour] = dd[ dd.hour == hour ]['count'].values

    plt.figure(figsize=(20,10))
    plt.ylabel("Count rent")
    plt.xlabel("Hours")
    plt.title("count vs hours\n" + message)
    plt.boxplot( [hours[hour] for hour in range(24)] )
    
    axis = plt.gca()
    axis.set_ylim([1, 1100])


plot_hours( train[train.datetime.dt.year == 2011], 'year 2011') # box plot for hourly count for the mentioned year
plot_hours( train[train.datetime.dt.year == 2012], 'year 2012') # box plot for hourly count for the mentioned year


dt = pd.to_datetime(train["datetime"]) # converting the column to datetime for train dataset
train["hour"] = dt.map(lambda x: x.hour) # adding the hour column for train dataset
train.head()


dt_test = pd.to_datetime(test["datetime"]) # converting the column to datetime for test dataset
test["hour"] = dt_test.map(lambda x: x.hour) # adding the hour column for test dataset
test.head()


plot_hours( train[train.workingday == 1], 'working day') # plotting hourly count of rented bikes for working days for a given year
plot_hours( train[train.workingday == 0], 'non working day') # plotting hourly count of rented bikes for non-working days for a given year


# method to convert categorical data to numerical data
def categorical_to_numeric(x):
    if 0 <=  x < 6:
        return 0
    elif 6 <= x < 13:
        return 1
    elif 13 <= x < 19:
        return 2
    elif 19 <= x < 24:
        return 3


train['hour'] = train['hour'].apply(categorical_to_numeric)# applying the above conversion logic to training data
train.head()


test['hour'] = test['hour'].apply(categorical_to_numeric) # applying the above conversion logic to test data
test.head()


# drop unnecessary columns

train = train.drop(['datetime'], axis=1)
test = test.drop(['datetime'], axis=1)


train.head()


# an Hour bs Count Graph depicting average bike demand based on the hour 
figure,axes = plt.subplots(figsize = (10, 5))
hours = train.groupby(["hour"]).agg("mean")["count"]  
hours.plot(kind="line", ax=axes) 
plt.title('Hours VS Counts')
axes.set_xlabel('Time in Hours')
axes.set_ylabel('Average of the Bike Demand')
plt.show()


# count of different temp values
a = train.groupby('temp')[['count']].mean()
a


a.plot()
plt.show()


# count of different atemp values
a = train.groupby('atemp')[['count']].mean()
a


a.plot()
plt.show()


# count based on holiday
a = train.groupby('holiday')[['count']].mean()


a.plot()
plt.show()


# method to  select the features. If a feature is not in the blaklist, it gets selected
def select_features(data):
    black_list = ['casual', 'registered', 'count', 'is_test', 'datetime', 'count_log']
    return [feat for feat in data.columns if feat not in black_list]


# a method to show results of various model and their predictions
def _simple_modeling(X_train, X_test, y_train, y_test):
    # sepcifying the model names
    models = [
        ('dummy-mean', DummyRegressor(strategy='mean')),
        ('dummy-median', DummyRegressor(strategy='median')),
        ('random-forest', RandomForestRegressor(random_state=0)),
    ]
    
    results = []

    for name, model in models:
        model.fit(X_train, y_train)# fitting the training data to model
        y_pred = model.predict(X_test) # doing predictions using the model
        
        results.append((name, y_test, y_pred)) # creating the list of predictions from various models
        
    return results

# a method to return the performance metric of the model used in the above method
def simple_modeling(X_train, X_test, y_train, y_test):
    results = _simple_modeling(X_train, X_test, y_train, y_test) # using the function defined above to caluclate the predictions
    
    return [ (r[0], rmsle(r[1], r[2]) ) for r in results] # returning the performance metrics


from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import cross_val_score
forest_reg = RandomForestRegressor(n_estimators=100) # instantiating the random Forest Regressor

score = cross_val_score(forest_reg, train, train, cv=4) # calcuating the cross validation score
print (score)

[0.99516611 0.99725277 0.99769427 0.9973147 ]

	datetime	season	weather	temp	atemp	humidity	casual	registered	count
0	2011-01-01 00:00:00	1	1	9.84	14.395	81	3	13	16
1	2011-01-01 01:00:00	1	1	9.02	13.635	80	8	32	40
2	2011-01-01 02:00:00	1	1	9.02	13.635	80	5	27	32
3	2011-01-01 03:00:00	1	1	9.84	14.395	75	3	10	13
4	2011-01-01 04:00:00	1	1	9.84	14.395	75	0	1	1

	datetime	season	workingday	weather	temp	atemp	humidity	windspeed	casual	registered	count
10881	2012-12-19 19:00:00	4	1	1	15.58	19.695	50	26.0027	7	329	336
10882	2012-12-19 20:00:00	4	1	1	14.76	17.425	57	15.0013	10	231	241
10883	2012-12-19 21:00:00	4	1	1	13.94	15.910	61	15.0013	4	164	168
10884	2012-12-19 22:00:00	4	1	1	13.94	17.425	61	6.0032	12	117	129
10885	2012-12-19 23:00:00	4	1	1	13.12	16.665	66	8.9981	4	84	88

	season	holiday	workingday	weather	temp	atemp	humidity	windspeed	casual	registered	count
count	10886.000000	10886.000000	10886.000000	10886.000000	10886.00000	10886.000000	10886.000000	10886.000000	10886.000000	10886.000000	10886.000000
mean	2.506614	0.028569	0.680875	1.418427	20.23086	23.655084	61.886460	12.799395	36.021955	155.552177	191.574132
std	1.116174	0.166599	0.466159	0.633839	7.79159	8.474601	19.245033	8.164537	49.960477	151.039033	181.144454
min	1.000000	0.000000	0.000000	1.000000	0.82000	0.760000	0.000000	0.000000	0.000000	0.000000	1.000000
25%	2.000000	0.000000	0.000000	1.000000	13.94000	16.665000	47.000000	7.001500	4.000000	36.000000	42.000000
50%	3.000000	0.000000	1.000000	1.000000	20.50000	24.240000	62.000000	12.998000	17.000000	118.000000	145.000000
75%	4.000000	0.000000	1.000000	2.000000	26.24000	31.060000	77.000000	16.997900	49.000000	222.000000	284.000000
max	4.000000	1.000000	1.000000	4.000000	41.00000	45.455000	100.000000	56.996900	367.000000	886.000000	977.000000

	datetime	season	workingday	weather	temp	atemp	humidity	windspeed
0	2011-01-20 00:00:00	1	1	1	10.66	11.365	56	26.0027
1	2011-01-20 01:00:00	1	1	1	10.66	13.635	56	0.0000
2	2011-01-20 02:00:00	1	1	1	10.66	13.635	56	0.0000
3	2011-01-20 03:00:00	1	1	1	10.66	12.880	56	11.0014
4	2011-01-20 04:00:00	1	1	1	10.66	12.880	56	11.0014

	datetime	season	workingday	weather	temp	atemp	humidity	windspeed
6488	2012-12-31 19:00:00	1	1	2	10.66	12.880	60	11.0014
6489	2012-12-31 20:00:00	1	1	2	10.66	12.880	60	11.0014
6490	2012-12-31 21:00:00	1	1	1	10.66	12.880	60	11.0014
6491	2012-12-31 22:00:00	1	1	1	10.66	13.635	56	8.9981
6492	2012-12-31 23:00:00	1	1	1	10.66	13.635	65	8.9981

	season	holiday	workingday	weather	temp	atemp	humidity	windspeed
count	6493.000000	6493.000000	6493.000000	6493.000000	6493.000000	6493.000000	6493.000000	6493.000000
mean	2.493300	0.029108	0.685815	1.436778	20.620607	24.012865	64.125212	12.631157
std	1.091258	0.168123	0.464226	0.648390	8.059583	8.782741	19.293391	8.250151
min	1.000000	0.000000	0.000000	1.000000	0.820000	0.000000	16.000000	0.000000
25%	2.000000	0.000000	0.000000	1.000000	13.940000	16.665000	49.000000	7.001500
50%	3.000000	0.000000	1.000000	1.000000	21.320000	25.000000	65.000000	11.001400
75%	3.000000	0.000000	1.000000	2.000000	27.060000	31.060000	81.000000	16.997900
max	4.000000	1.000000	1.000000	4.000000	40.180000	50.000000	100.000000	55.998600

	count
temp
0.82	77.714286
1.64	91.500000
2.46	43.000000
3.28	19.272727
4.10	50.272727
4.92	58.416667
5.74	53.233645
6.56	68.109589
7.38	67.754717
8.20	81.995633
9.02	73.616935
9.84	86.442177
10.66	92.560241
11.48	111.066298
12.30	120.002597
13.12	148.547753
13.94	145.053269
14.76	152.957173
15.58	179.682353
16.40	170.217500
17.22	182.609551
18.04	160.878049
18.86	159.692118
19.68	185.058824
20.50	204.672783
21.32	196.480663
22.14	184.717122
22.96	212.392405
23.78	235.650246
24.60	237.182051
25.42	222.062035
26.24	232.403974
27.06	211.025381
27.88	203.433036
28.70	257.679157
29.52	277.691218
30.34	303.193980
31.16	352.801653
31.98	318.683673
32.80	355.623762
33.62	348.323077
34.44	340.225000
35.26	342.934211
36.08	362.869565
36.90	318.717391
37.72	332.176471
38.54	238.857143
39.36	317.833333
41.00	294.000000

	count
atemp
0.760	1.000000
1.515	3.000000
2.275	38.000000
3.030	82.285714
3.790	39.062500
4.545	66.090909
5.305	63.200000
6.060	64.876712
6.820	56.380952
7.575	55.933333
8.335	58.444444
9.090	80.000000
9.850	81.456693
10.605	95.951807
11.365	90.442804
12.120	102.656410
12.880	89.518219
13.635	94.308017
14.395	116.483271
15.150	133.967456
15.910	133.897638
16.665	148.509186
17.425	147.799363
18.180	133.585366
18.940	149.555556
19.695	179.682353
20.455	170.217500
21.210	182.609551
21.970	160.878049
22.725	159.692118
23.485	185.058824
24.240	204.672783
25.000	195.109589
25.760	179.626478
26.515	212.392405
27.275	200.503546
28.030	133.312500
28.790	142.771429
29.545	151.046693
30.305	227.291429
31.060	308.323398
31.820	258.655518
32.575	331.746324
33.335	244.107143
34.090	295.183036
34.850	277.448763
35.605	312.144654
36.365	349.243902
37.120	334.144068
37.880	351.835052
38.635	335.783784
39.395	319.194030
40.150	369.577778
40.910	324.512821
41.665	281.434783
42.425	301.958333
43.180	307.142857
43.940	215.428571
44.695	354.333333
45.455	312.000000